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The application of multiscale and stochastic techniques to the solution of a linearized inverse scattering problem is presented. This approach allows for the explicit and easy handling of many difficulties associated with problems of this type. Regularization is accomplished via the use of a multiscale prior stochastic model which offers considerable flexibility for the incorporation of prior knowledge and constraints. We use the relative error covariance matrix (RECM), introduced in [28], as a tool for quantitatively evaluating the manner in which data contribute to the structure of a reconstruction. Given a set of scattering experiments, the RECM is used for understanding and analyzing the process of data fusion and allows us to define the space-varying optimal scale for reconstruction as a function of the nature (resolution, quality, and distribution of observation points) of the available measurement sets. Examples of our multiscale inversion algorithm are presented using the Born approximation of an inverse electrical conductivity problem formulated so as to illustrate many of the features associated with inverse scattering problems arising in fields such as geophysical prospecting and medical imaging.

Potentiometric stripping analysis (PSA) and atomic absorption spectroscopy (AAS) results in an analysis of lead and cadmium in soil agree best among the four techniques, which also include inductively coupled plasma-atomic emission spectroscopy (ICP-AES) and X-ray fluorescence (XRF). Concentrations measured by PSA were higher than those found by AAS, ICP-AES and XRF. Concentrations determined by XRF were lower or equal to those measured by ICP-AES and AAS.

Emerging bacterial pathogens threaten global health and food security, and so it is important to ask whether these transitions to pathogenicity have any common features. We present a systematic study of the claim that pathogenicity is associated with genome reduction and gene loss. We compare broad-scale patterns across all bacteria, with detailed analyses of Streptococcus suis , an emerging zoonotic pathogen of pigs, which has undergone multiple transitions between disease and carriage forms. We find that pathogenicity is consistently associated with reduced genome size across three scales of divergence (between species within genera, and between and within genetic clusters of S. suis). Although genome reduction is also found in mutualist and commensal bacterial endosymbionts, genome reduction in pathogens cannot be solely attributed to the features of their ecology that they share with these species, that is, host restriction or intracellularity. Moreover, other typical correlates of genome reduction in endosymbionts (reduced metabolic capacity, reduced GC content, and the transient expansion of nonfunctional elements) are not consistently observed in pathogens. Together, our results indicate that genome reduction is a consistent correlate of pathogenicity in bacteria. [ABSTRACT FROM AUTHOR]

An adaptive two-pass rank order filter to remove impulse noises in highly corrupt images is presented. The working process of this adaptive filter based on the standard median filter, center-weighted median filter (CWMF), adaptive weighted median filter, lower-upper-middle (LUM) filter and soft-decision rank-order-mean (SD-ROM) filter, by analyzing the spatial distribution of estimated impulse noise is discussed.

The electromagnetic scattering from a three-dimensional (3-D) shallow object buried under a two-dimensional (2-D) random rough dielectric surface is analyzed in this work. The buried object can be a perfect electric conductor (PEC) or can be a penetrable dielectric with size and burial depth comparable to the free-space wavelength. The random rough ground surface is characterized with Gaussian statistics for surface height and for surface autocorrelation function. The Poggio, Miller, Chang, Harrington, and Wu (PMCHW) integral equations are implemented and extended in this work. The integral equation-based steepest descent fast multipole method (SDFMM), that was originally developed at UIUC, has been used and the computer code based on this algorithm has been successfully modified to handle the current application. The significant potential of the SDFMM code is that it calculates the unknown moment method surface electric and magnetic currents on the scatterer in a dramatically fast, efficient, and accurate manner. Interactions between the rough surface interface and the buried object are fully taken into account with this new formulation. Ten incident Gaussian beams with the same elevation angle and different azimuth angles are generated for excitation as one possible way of having multiple views of a given target. The scattered electric fields due to these ten incident beams are calculated in the near zone and their complex vector average over the multiple views is computed. The target signature is obtained by subtracting the electric fields scattered from the rough ground only from those scattered from the ground with the buried anti-personnel mine. Significant polarization dependency is observed for the PEC object signature compared with that of the penetrable object, which can be used in target discrimination. Moreover, fields scattered above the rough ground experience significantly more distortion than fields transmitted into the ground. Index Terms--Buried-target, computational electromagnetics, fast multipole method (FMM), mine detection, rough surface scattering.

In this paper, we present an algorithm for the detection and localization of an unknown number of objects buried in a halfspace and present in the near field of a linear receiver array. To overcome the nonplanar nature of the wavefield over the array, the full array is divided into a collection of subarrays such that the scattered fields from objects are locally planar at each subarray. Using the multiple signal classification (MUSIC) algorithm, directions of arrival (DOA) of locally planar waves at each subarray are found. By triangulating these DOA's, a set of crossings, condensed around expected object locations, are obtained. To process this spatial crossing pattern, we develop a statistical model for the distribution of these crossings and employ hypotheses testing techniques to identify a collection of small windows likely to contain targets. Finally, the results of the hypothesis tests are used to estimate the number and locations of the targets. Using simulated data, we demonstrate the usefulness and performance of this approach for typical background electrical properties and signal to noise ratios. Index Terms--Array processing, hypothesis testing, subsurface object detection.

The problem of characterizing the geometric structure of an object buried in an inhomogeneous halfspace of unknown composition is considered. We develop a nonlinear inverse scattering algorithm based on a low-dimensional parameterization of the unknown object and the background. In particular, we use a low-order polynomial expansion to represent the spatial variations in the real and imaginary parts of the object and background complex permittivities. The boundary separating the target from the unknown background is described using a periodic, quadratic B-spline curve whose control points can be individually manipulated. We determine the unknown control point locations and contrast expansion coefficients using a greedy-type approach to minimize a regularized least-squares cost function. The regularizer used here is designed to constrain the geometric structure of the boundary of the object and is closely related to snake methods employed in the image processing community. We demonstrate the performance of our approach via extensive numerical simulation involving two-dimensional (2-D), [TM.sub.z] scattering geometries. Our results indicate a strong ability to localize and estimate the shape of the object even in the presence of an unknown and inhomogeneous background. Index Terms--B-splines, clutter models, inverse scattering, mine detection, shape-based methods.

Cavitation-facilitated microbubble-mediated focused ultrasound therapy is a promising method of drug delivery across the blood-brain barrier (BBB) for treating many neurological disorders. Unlike ultrasound thermal therapies, during which magnetic resonance thermometry can serve as a reliable treatment control modality, real-time control of modulated BBB disruption with undetectable vascular damage remains a challenge. Here a closedloop cavitation controlling paradigm that sustains stable cavitation while suppressing inertial cavitation behavior was designed and validated using a dual-transducer system operating at the clinically relevant ultrasound frequency of 274.3 kHz. Tests in the normal brain and in the F98 glioma model in vivo demonstrated that this controller enables reliable and damage-free delivery of a predetermined amount of the chemotherapeutic drug (liposomal doxorubicin) into the brain. The maximum concentration level of delivered doxorubicin exceeded levels previously shown (using uncontrolled sonication) to induce tumor regression and improve survival in rat glioma. These results confirmed the ability of the controller to modulate the drug delivery dosage within a therapeutically effective range, while improving safety control. It can be readily implemented clinically and potentially applied to other cavitation-enhanced ultrasound therapies. [ABSTRACT FROM AUTHOR]